In sequential analyses sequences of items are extracted from transactional electronic data. As more and more transactional data are acquired electronically sequential analysis becomes more important.
A practical example of a sequential analysis is the clickstream analysis for calculating typical user paths of Web site users. Further examples of sequential analyses are text analysis for extracting characteristic series of words in electronically memorized documents and the analysis of baskets of merchandise taking into account the order of products in trade to define typical product chain purchases. Moreover, sequential analyses are widely used in chemistry and genetics.
A great number of specialized methods of sequential analysis are known to date, especially in genetics. However, very few of them are universally applicable. In the simplest case, all variants of possible sequences are studied as regards their frequency. But for reasons of computer capacity that can be done only for small amounts of data. Sequential analysis algorithms based on search trees present an alternative, such as the algorithm “Capri” by Messrs. Lumio. Yet their speed, too, is insufficient for analyzing large transaction data. Methods based on the classical analysis of baskets of merchandise offer a quicker alternative and have already been used for sequential analysis of baskets of merchandise (Agrawal R., Srikant R., Mining Sequential Patterns. IBM Almaden Research Center, 650 Harry Road, San Jose).